Time series forecasting is essential for decision-making in fields like economics, supply chain management, and healthcare. While traditional statistical methods and machine learning have been used, deep learning architectures such as MLPs, CNNs, RNNs, and GNNs have offered new solutions but faced limitations due to their inherent biases. Transformer models have been prominent for handling long-term dependencies, yet recent studies suggest that simpler models like linear layers can sometimes outperform them. This has led to a renaissance in architectural modeling, with a focus on hybrid and emerging models such as diffusion, Mamba, and foundation models. The exploration of diverse architectures addresses challenges like channel dependency and distribution shift, enhancing forecasting performance and offering new opportunities for both newcomers and seasoned researchers in time series forecasting. This matters because improving time series forecasting can significantly impact decision-making processes across various critical industries.
